Find research datasets worth reusing

opencc-by-4.0Oct 2024View details →

Facile synthesis of CuxS electrocatalysts for CO2 conversion into formate and study of relations between Cu and S with the selectivity

Datasets for figures provided in the manuscript main text.

opencc-zeroAug 2021View details →

A Repackaged Taxonomic Backbone of Global Biodiversity Information Facility (GBIF)

Publication date: 2022-12-06T07:37:19-06:00 A Repackaged Taxonomic Backbone of Global Biodiversity Information Facility (GBIF) --- Global Biodiversity Information Facility (GBIF) facilitates access to billions of biodiversity data records. These records include detailed accounts of life on earth. To help records of specific life forms, GBIF provides a taxonomic backbone [1,2]. This backbone contains a long list of names used to describe species and associated hierarchies and taxonomic publications. These lists are sourced from datasets around the world. At time of writing (6 Dec 2022), GBIF publishes a simplified version of their taxonomic backbone at [https://hosted-datasets.gbif.org/datasets/backbone/](https://hosted-datasets.gbif.org/datasets/backbone/) [1]. This repository provides script to pre-process https://hosted-datasets.gbif.org/datasets/backbone/current/simple.txt.gz to help facilitate access and improve performance of the creation of search indexes. Pre-process steps currently include: 1. reducing amount of columns 2. reverse sort by id 3. reverse sort by name Contents --- README:     this file repackage-gbif-backbone.sh:     script used to repackage GBIF Simple Backbone. repackage-gbif-backbone.log:     log of repackaging of GBIF Simple Backbone. backbone-current-simple.txt.gz:     original GBIF backbone archive gbif-backbone-by-name.tsv.gz:     two columns, gzipped, tab-separated text file with columns name, and id     reverse sorted by name  gbif-backbone-by-name.tsv.sha256:     sha256 hash of the uncompressed gbif-backbone-by-name.tsv.gz gbif-backbone-by-id.tsv.gz:     20 columns, gzipped, tab-separated text file with first 20 columns of repackaged GBIF backbone file     reverse sorted by id gbif-backbone-by-id.tsv.sha256:     sha256 hash of the uncompressed gbif-backbone-by-id.tsv.gz References --- [1] Simplied GBIF Backbone Taxonomy. Accessed at https://hosted-datasets.gbif.org/datasets/backbone/ on 2022-12-06. [2] GBIF Secretariat (2021). GBIF Backbone Taxonomy. Checklist dataset https://doi.org/10.15468/39omei accessed via GBIF.org on 2021-08-18. Hash URIs --- This publication includes the following content uris: hash://sha256/82d5f2153b4533322692d95eeb18b0f103e1b2297e38bd9ea935b07ba86cd7d5 hash://sha256/50c155f66efb2efba0b8b624f8541e81cbe16a701d420a5073791fb993f72919 hash://sha256/9cd7d4c91292d86c726210446cd6fe45602505a7c0ea3b7c4f4f481f85f193ad (uncompressed) hash://sha256/f950dde25cce9ba9cce67caa1c68ce0c99cb31fe2dc9658fec85a987d9f31654 hash://sha256/f21c6b90f17c6083fcfb4853f3c581dcc2aadd291691fa128392a205321f420b (uncompressed) hash://sha256/5e0a4d1d2d1cccbdcc6b2c9831fafe61c54eb055f2d13ec40d9ac161889b9f89 hash://sha256/f6e477133d0585706ee5522963b204200cb3cd198f011cbf62be0fa8519763b5 (uncompressed)

opencc-by-4.0Feb 2023View details →

Supplying renewable energy to Central European research facilities: A techno-economic comparison of electricity and hydrogen (Dataset)

This dataset contains central input assumptions and results related to the publication "Supplying renewable energy to Central European research facilities: A techno-economic comparison of electricity and hydrogen". Result files are contained in the results.zip archive file. The file contains for each scenario, as indicated by the folder structure, the following files: <ul> <li>results.csv: Central scenario results exported as character separated value (csv) file, with a semicolon (;) as field separator. All fields are quoted using double quotation marks "...". Can be explored using standard office software like Microsoft Excel/Libre Office or other tools.</li> <li>network.nc: PyPSA network file containing the optimized scenario with all input and unprocessed outputs (results). Can be explored using the <a href="https://pypsa.readthedocs.io">PyPSA software package</a>.</li> <li>lcoes.csv: Levelised Cost of Electricity used to construct the renewable energy source (RES) based supply curve for each scenario.</li> </ul> The dataset further contains the following files which represent central input assumptions to the model and scenarios, both as CSV files: <ul> <li>efficiencies.csv: Technology process and conversion efficiencies including more details on the assumptions and information on which references the assumptions are based.</li> <li>costs_2030.csv: Technology cost assumptions for 2030 including more details on the assumptions and information on which references the assumptions are based. This data is based on this <a href="https://github.com/pypsa/technology-data">Technology Data repository</a> on GitHub.</li> </ul>

opencc-zeroMar 2023View details →

Global Biodiversity Information Facility (GBIF): an exhaustive list of gbif record ids, dataset keys, and their associated Occurrence IDs, Institution Code, Collection Codes and Catalog Numbers. hash://sha256/ea88f03a7bfd1ba853fdbea3203d54ab81ac3cdc8e8da7c96bbbba9c4b05d933 hash://md5/c49fe34785354847b37ea4509261e130

The Global Biodiversity Information Facility (GBIF) indexes thousands of biodiversity datasets from Natural History Collections, citizen science initiatives (e.g., iNaturalist, eBird), and other sources. As part of the index process, GBIF associates at least two identifiers with indexed records: a record id (aka gbifID) and a dataset id (aka dataset key). These ids are central to do lookup, reference data, and package interpreted data products. This publication contains an exhaustive list of GBIF IDs and ids associated by their data providers as derived from: GBIF.org (01 March 2023) GBIF Occurrence Download https://doi.org/10.15468/dl.pk3trq The resource (size: ~260GB) provided by GBIF had content id hash://sha256/c8bac8acb28c8524c53589b3a40e322dbbbdadf5689fef2e20266fbf6ddf6b97 and was used to generate the resource included in this publication using <pre><code class="language-bash">preston cat 'zip:hash://sha256/c8bac8acb28c8524c53589b3a40e322dbbbdadf5689fef2e20266fbf6ddf6b97!/0015281-230224095556074.csv'\ | cut -f 1,2,3,37,38,39\ | gzip\ > gbifid.tsv.gz </code></pre> with the content id of gbifid.tsv.gz (size: ~35GB) being hash://sha256/a339e32e10edaad585f61f2ded06cbb23e0618c65a6360db18d7d729054940a8 . the first 10 lines of gbifid.tsv.gz as extracted via <pre><code>preston cat --remote https://zenodo.org/record/7789866/files,https://linker.bio hash://sha256/a339e32e10edaad585f61f2ded06cbb23e0618c65a6360db18d7d729054940a8\ | gunzip\ | head</code></pre> are: <pre><code>gbifID datasetKey occurrenceID institutionCode collectionCode catalogNumber 2997162320 c71c8000-9fc7-422c-804a-ce6abe751771 3399442 CEPEC CEPEC CEPEC00109669 2997162309 c71c8000-9fc7-422c-804a-ce6abe751771 2733085 CEPEC CEPEC CEPEC00000818 2997162317 c71c8000-9fc7-422c-804a-ce6abe751771 2733086 CEPEC CEPEC CEPEC00000888 2997162313 c71c8000-9fc7-422c-804a-ce6abe751771 3399443 CEPEC CEPEC CEPEC00109744 2997162306 c71c8000-9fc7-422c-804a-ce6abe751771 2733087 CEPEC CEPEC CEPEC00000889 2997162316 c71c8000-9fc7-422c-804a-ce6abe751771 3399440 CEPEC CEPEC CEPEC00109605 2997162324 c71c8000-9fc7-422c-804a-ce6abe751771 2733088 CEPEC CEPEC CEPEC00000890 2997162308 c71c8000-9fc7-422c-804a-ce6abe751771 3399441 CEPEC CEPEC CEPEC00109615 2997162303 c71c8000-9fc7-422c-804a-ce6abe751771 2733089 CEPEC CEPEC CEPEC00000891</code></pre> Note that at time of writing, the html resource associated with the occurrence id 2997162320, and data set key c71c8000-9fc7-422c-804a-ce6abe751771 (extracted from of the first data row example above) are available via: https://gbif.org/occurrence/2997162320 and https://gbif.org/dataset/c71c8000-9fc7-422c-804a-ce6abe751771 respectively. This resource was initially created to help integrate with Bionomia (https://bionomia.net) to help associate people identifiers provided by bionomia to their original records via their GBIF ids. Bionomia re-uses GBIF records ids as a way to define links between records and the people (e.g., curators, collectors, identifiers) that worked on them.  In other words, this resource provides a versioned translation table from the GBIF data universe (as defined by GBIF record ids, and dataset keys) to the data collections that exist (and evolve) independent of it.  Note that the resource identified by hash://sha256/c8bac8acb28c8524c53589b3a40e322dbbbdadf5689fef2e20266fbf6ddf6b97 was not included in this publication it was too big (260GB) to fit. You may be able to retrieve the resource from its original location at https://api.gbif.org/v1/occurrence/download/request/0015281-230224095556074.zip .

opencc-by-4.0Apr 2023View details →

Raw data for the computation of ExPaNDS facilities maturity wrt FAIR data catalogues

Raw data for the computation of ExPaNDS facilities maturity wrt FAIR data catalogues. The method is explained in the <a href="https://doi.org/10.5281/zenodo.4146819">report on status, gap analysis and roadmap towards harmonised and federated metadata catalogues for EU national Photon and Neutron RIs</a>.

opencc-by-4.0Jun 2023View details →

A high-throughput 3D X-ray histology facility for biomedical research and preclinical applications - Underlying Data

Video files and logs Single-slice and thick-slice roll* source videos are included. Each video is accompanied by a .txt log that contains information about the source file, slice thickness, and a brief description of the visualization mode. List of files: <ul> <li>20211019-23h59m_20xAvgInt.mp4</li> <li>20211019-23h59m_20xAvgInt.txt</li> <li>20211019-23h59m_20xMaxInt.mp4</li> <li>20211019-23h59m_20xMaxInt.txt</li> <li>20211019-23h59m_20xStDev.mp4</li> <li>20211019-23h59m_20xStDev.txt</li> <li>20211019-23h59m_XYSliceRoll.mp4</li> <li>20211019-23h59m_XYSliceRoll.txt</li> <li>20211019-23h59m_XZSliceRoll.mp4</li> <li>20211019-23h59m_XZSliceRoll.txt</li> <li>20211019-23h59m_YZSliceRoll.mp4</li> <li>20211019-23h59m_YZSliceRoll.txt</li> </ul> * Thick-slice rolling is a 2D thick-slice viewing that allows rolling of a pre-selected number of slices (n) along the z-axis of the 3D data. A single thick-slice roll forwards is accomplished by translating the thick-slice by one single slice forwards; that is moving forward by one (+1) slice from the first and nth element and reapplying the criteria or operations to the new slice sub-stack. Volume XRH data These are processed raw volume file saved in .raw and/or .tiff format, which are resliced to a histology-relevant orientation and/or have been enhanced using noise reduction (3D median filter) and/or ct-artefact removal techniques (e.g. cBC identifies a bandpass filter used to remove intensity variations originating from the histology cassette). List of volume files: <ul> <li>32220_20200703_XRH_2504_OLK_DEMO02019-FFPE_1620x1959x164x16bit.raw <ul> <li>sample: Human lung adenocarcinoma</li> <li>histology-relevant resliced volume (2x2x2 3D medial filter applied)</li> <li>import as 1620 x 1959 x 164 x 16-bit, big-endian; voxel edge size (mm): 0.0160042 isotropic</li> </ul> </li> <li>cBC_32220_20200703_XRH_2504_OLK_DEMO02019-FFPE_1588x1674x164x16bit.raw <ul> <li>sample: Human lung adenocarcinoma</li> <li>cassette artefacts background correction (bandpass) of volume 32220_20200703_XRH_2504_OLK_DEMO02019-FFPE_1620x1959x164x16bit.raw</li> <li>import as 1620 x 1959 x 164 x 16-bit, big-endian; voxel edge size (mm): 0.0160042 isotropic</li> </ul> </li> <li>Med3D_HPass_2111_20190606_MEDX_2234_EH_HN2_recon_2000x1952x501x32bit.raw <ul> <li>sample: Human head and neck tumour</li> <li>histology-relevant resliced volume (1x1x1 3D medial filter applied)</li> <li>import as 2000 x 1952 x 501 x 32-bit, big-endian; voxel edge size (mm): 0.00999782 isotropic</li> </ul> </li> </ul> Conventional Histology and correlative imaging <ul> <li>HN2_Level001_MEDX080_Manual_BW_Series4.tif <ul> <li>H&E histology slice of the human head and neck tumour sample shown in "Med3D_HPass_2111_20190606_MEDX_2234_EH_HN2_recon_2000x1952x501x32bit.raw"</li> </ul> </li> <li>HN2_Level001_MEDX080_Manual_BW <ul> <li>manual landmark selection used for registering the conventional histology slice onto the μCT slice</li> </ul> </li> <li>HN2_MEDX_rotated_0080.tif <ul> <li>Slice 80 from volume "Med3D_HPass_2111_20190606_MEDX_2234_EH_HN2_recon_2000x1952x501x32bit.raw" that corresponds to histological slice "HN2_Level001_MEDX080_Manual_BW"</li> </ul> </li> </ul>

opencc-by-4.0Sep 2023View details →

MCU data in a Cypress 65 nm SRAM from heavy ions and protons collected at ground facilities

The dataset contains the raw MCU data collected at ground facilities under heavy ion and proton irradiation in the scope or RADSAGA and RADNEXT project. The device under consideration is the CY62167GE30-45ZXI, a 65 nm commercial SRAM available from Infineon (formerly Cypress). Note that the internal ECC has been disabled for this data collection. More information on data collection are available through this paper (<a href="https://doi.org/10.1109/REDW51883.2020.9325822">10.1109/REDW51883.2020.9325822</a>). The MCU were determined through the procedure explained in these two papers (<a href="https://doi.org/10.1109/TNS.2014.2313742">10.1109/TNS.2014.2313742</a> and <a href="https://doi.org/10.1109/TNS.2015.2496874">10.1109/TNS.2015.2496874</a>).

edi44/100

Nevada Desert FACE Facility Soil Organic Carbon Data

This data set is the result of soils analysis from the Nevada Desert Free-Air CO2 Enrichment Facility (NDFF) experiment in the Mojave Desert and reports soil organic carbon (%C) and delta 13C stable isotope values. These soils were collected at the end of the NDFF experiment in 2007 and stored at Cornell University until analysis in 2018. Soils were harvested from 6 cover types (5 perennial vegetation covers and unvegetated interspace soils) from 0-100 cm in the soil profile in 20 cm increments. Soils were pretreated for inorganic carbon removal using an acid fumigation technique with HCl. Bulk density from NDFF plots is provided (kg soil* ha ^ -1) so that SOC stocks may be calculated. These data provide the basis for a publication challenging the prevailing idea that arid ecosystems will increase soil organic carbon stocks under long term elevated CO2.

openCC0Aug 2022View details →

opencc-by-4.0Dec 2017View details →

Morphodynamic stability of river and tidal bifurcations around bars tested in the Fast Flow Facility

Multithread rivers such as the Jamuna and Mekong have networks of channels and bars that change with every flood. Tidal systems such as the Scheldt, Humber and Columbia estuaries and short tidal basins in the Wadden Sea and in Florida, have perpetually changing and interacting channels and shoals formed by ebb and flood currents. Current models fail to forecast these natural dynamics, yet main channels are economically important shipping fairways, whilst shoal areas that emerge and submerge daily are ecologically valuable habitats. Human interference, changing river discharge and sealevel rise threaten all functions. Furthermore, there are strong indications that fairway deepening leads to reduced urban safety due to enhanced flow resistance by groynes in rivers and enhanced tidal range in estuaries (e.g. Bolla Pittaluga et al. 2015 in AWR, Seminara et al., in EH 2011). This enhances dike failure risk during low water level and flooding during high water level. We urgently need dynamic forecasting models to optimise management strategies for these multiple functions (Wang et al. 2012 in Ocean Coastal Manage., Coco et al. 2013 in Mar. Geol.). Here we target firstly river bifurcations and secondly the mutually evasive ebb- or flood-dominated channels that form around bars and are found in all sandy tidal systems in the world (van Veen 1950/2002 in J. R. Dutch Geograph. Soc.). The cause for the mutual evasion is still incompletely understood despite the fact that they also appear in our numerical model results and experiments (Canestrelli et al., in JGR 2010; Kleinhans et al. 2015 in JGR). The nodes where ebb and flood channels connect can be seen as asymmetric bifurcations where one channel is preferred during ebb and the other during flood. Such bifurcations are critical elements that partition flow and sediment through the channel network, govern bar merging and splitting and are locations where bed steps form in shipping lanes, as in river bifurcations. Stability and equilibrium configurations are mostly unknown for tidal bifurcations except for one recent theory (Wang et al in prep.). In particular, we have a fair understanding of the tidal dynamics, but this is incomplete for the morphodynamics, especially related to understanding the sediment division at the bifurcation. We take advantage of the better but yet incomplete understanding of river bifurcations. The stability of river bifurcations has been studied for two decades in fieldwork, experimentation, linear stability theory and numerical modelling (e.g. Wang et al. 1995, JHR, see review in Kleinhans et al. 2013, ESPL) and our recent theory (Bolla Pittaluga et al. 2015 in GRL) synthesises many of the earlier results as follows: In bedload-dominated rivers, symmetrical bifurcations are unstable and develop towards a highly asymmetrical division of discharge and sediment. The same is the case for suspended sediment-dominated rivers, but the theory predicts stable bifurcations for intermediate sediment mobility. However, there is very little data for conditions intermediate between low and high mobility rivers. Moreover, we have no idea whether bifurcations in reversing tidal flow are unstable for similar configurations and conditions as in rivers. Here we mean configurations that are entirely free of topographic forcings on the flow: straight channels split into two channels over some length and depth. Our objective was therefore to experimentally investigate bifurcation stability in a range of sediment mobilities in unidirectional flow and reversing tidal flow ceteris paribus.

opencc-by-4.0Feb 2017View details →

Supplementary material 1: Global Biodiversity Information Facility: Taxa and Records from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063

All records in GBIF with taxonomic ranks (kingdom, phylum, class, order, and species), basis of record (e.g., preserved specimen), and count of records, exported from GBIF on 7 December 2014.

Fig. 5 in Freier Zugang zu den Informationen der Artenvielfalt - Wie werde ich Teil der Global Biodiversity Information Facility (GBIF)?

Fig. 5: Das Suchportal des Botanik-Knotens von GBIF-Deutschland, einer der mehreren im Internet verfügbaren Zugangspunkte zu den Daten des GBIF-Netzwerks.

Fig. 2 in Freier Zugang zu den Informationen der Artenvielfalt - Wie werde ich Teil der Global Biodiversity Information Facility (GBIF)?

Fig. 2:Tatenflüsse über das Internet im GBIF-Netzwerk zwischen Nutzer, Suchportal und Datenlieferant.

Fig. 3 in Freier Zugang zu den Informationen der Artenvielfalt - Wie werde ich Teil der Global Biodiversity Information Facility (GBIF)?

Fig. 3: Die Zuordnung der Daten aus dem relationalen Datenschema der Sammlungsdatenbank zu den ABCD-Elementen wird im Mapping festgelegt und ist in einer komfortablen Oberfläche mit dem Internet- Browser möglich.

Fig. 1 in Freier Zugang zu den Informationen der Artenvielfalt - Wie werde ich Teil der Global Biodiversity Information Facility (GBIF)?

Fig. 1: Die Wrapper-Software umgibt die bestehenden Sammlungsdatenbanken mit einer zusätzlichen Abstraktionsschicht und bietet so eine definierte Schnittstelle zwischen den existierenden Datenbanksystemen und den GBIF-Suchportalen.

Fig.1 in Die Global Biodiversity Information Facility (GBIF) - Struktur, Aufgaben und Ziele

Fig.1: Das Knotensystem GBIF Deutschland und seine Anbindung an GBIF International. Die Daten fliessen aus den Teilprojekten in die Datenbanksysteme der einzelnen Knoten, denen ein BioCASE-Wrapper aufgesetzt ist, der auf dem ABCD-Datenmodell basiert. Damit ist es möglich, alle angebundenen Daten über das Datenportal von GBIF International im Internet abzurufen bzw. verfügbar zu machen. GBIF International stellt ausserdem die Wrapper-Software DiGIR, welche auf dem Darwin Core 2 aufbaut, zur Verfügung. Einzelne Teilprojekte, wie z.B. DIG mit BIODAT im Knoten Evertebraten I, setzen eigene Datenbanklösungen ein und fungieren daher als direkte GBIF Datenprovider. Die Angaben entsprechen dem Stand Anfang April 2005.

opencc-by-4.0Jan 2023View details →

Five-dimensional phase space measurement at the Spallation Neutron Source Beam Test Facility

This data set consists of 285,082 two-dimensional images which collectively describe the five-dimensional phase space distribution \(f(x, x', y, y', w)\) of a 2.5 MeV, -25.6 mA H\(^-\) ion beam in the <a href="https://neutrons.ornl.gov/sns">Spallation Neutron Source</a> Beam Test Facility (SNS-BTF). Here, \(x\) and \(y\) are the transverse positions, \(x' = dx/ds\) and \(y' = dy/ds\) are the transverse slopes, \(s\) is the position along the reference trajectory, and \(w\) is the deviation from the kinetic energy of the synchronous particle.The measurement plane is located in the medium energy beam transport (MEBT) section of the SNS-BTF, 1.3 meters after a radio-frequency quadrupole (RFQ). The measurement apparatus consists of three transverse slits (one horizontal, two vertical) and a 90-degree dipole bend followed by a fluorescent screen. The horizontal slit selects \(y\); two vertical slits select \(x\) and \(x'\); \(y'\)is a function of \(y\) and the vertical position on the screen, \(w\) is a function of \(x\), \(x'\), and the horizontal position on the screen. Thus, the image on the screen gives the density \(f(y', w)\) within a small three-dimensional region in \(x-x'-y\) space. The five-dimensional density is obtained by scanning the slits in a nested loop.The data set consists of 285,082 images (20 GB). Jupyter notebooks are included to interpolate the data on a regular grid in five-dimensional phase space, as well as to generate interactive figures. See 'README.md' for instructions. (The interpolated five-dimensional image is also included in a separate folder.)More information can be found in a corresponding publication: https://doi.org/10.1103/PhysRevAccelBeams.26.064202

opencc-by-4.0Jan 2022View details →

Spectrograms and frequencies of the first mode of Schumann Resonance according to multi-position monitoring parformed at the Ukrainian Antarctic Station (2002-2020) and at the Arctic SOUSY facility (2013-2020)

This dataset contains the processed data used for the publication: ELECTROMAGNETIC SEASONS IN SCHUMANN RESONANCE RECORDS. The dataset contains the daily spectrograms and frequensies of first mode of Schumann Resonance (derived for North-South and East-West magnetic components) of ELF signals recorded at the Ukrainian “Akademik Vernadsky” Antarctic station (65.25° N and 64.25° W) 2002-2020, and at SOUSY Arctic facility (Svalbard 78.15° N and 16.05° E) 2013-2020.

opencc-by-4.0Jun 2022View details →

A Repackaged Taxonomic Backbone of Global Biodiversity Information Facility (GBIF) - 2021-11-26

A Repackaged Taxonomic Backbone of Global Biodiversity Information Facility (GBIF) --- Global Biodiversity Information Facility (GBIF) facilitates access to billions of biodiversity data records. These records include detailed accounts of life on earth. To help records of specific life forms, GBIF provides a taxonomic backbone [1,2]. This backbone contains a long list of names used to describe species and associated hierarchies and taxonomic publications. These lists are sourced from datasets around the world. At time of writing (18 Aug 2021), GBIF publishes a simplified version of their taxonomic backbone at [https://hosted-datasets.gbif.org/datasets/backbone/](https://hosted-datasets.gbif.org/datasets/backbone/) [1]. This repository provides script to pre-process https://hosted-datasets.gbif.org/datasets/backbone/backbone-current-simple.txt.gz to help facilitate access and improve performance of the creation of search indexes. Pre-process steps currently include: 1. reducing amount of columns 2. reverse sort by id 3. reverse sort by name Contents --- README:     this file repackage-gbif-backbone.sh:     script used to repackage GBIF Simple Backbone. backbone-current-simple.txt.gz:     original GBIF backbone archive gbif-backbone-by-name.tsv.gz:     two columns, gzipped, tab-separated text file with columns name, and id     reverse sorted by name gbif-backbone-by-name.tsv.sha256:     sha256 hash of the uncompressed gbif-backbone-by-name.tsv.gz gbif-backbone-by-id.tsv.gz:     20 columns, gzipped, tab-separated text file with first 20 columns of repackaged GBIF backbone file     reverse sorted by id gbif-backbone-by-id.tsv.sha256:     sha256 hash of the uncompressed gbif-backbone-by-id.tsv.gz References --- [1] Simplied GBIF Backbone Taxonomy. Accessed at https://hosted-datasets.gbif.org/datasets/backbone/ on 2021-08-18. [2] GBIF Secretariat (2021). GBIF Backbone Taxonomy. Checklist dataset https://doi.org/10.15468/39omei accessed via GBIF.org on 2021-08-18. Hash URIs --- This publication includes the following content uris: repackage-gbif-backbone.sh:     hash://sha256/073ac5490252c4ccbbd4f516d391faebe62c9fde9e4d75ae870441a86c382527 backbone-current-simple.txt.gz:     hash://sha256/15cbfc038e666356af27248935f79e408ed51fd8c0b49a668fed8dbf72591502     hash://sha256/1f78788a4a046dcbcf1e36c7658a1e333ca60e7586a372238d58b938d91fde51 (uncompressed) gbif-backbone-by-name.tsv.gz:     hash://sha256/6e11ae9961a9498b60d4bdeb489d6c1f5da9c2732310edaecdc79bd287b79ef4     hash://sha256/934ce05dbd067abb209168bd1d9389f122d051e1b7374b5d757a12e86f8da9a5 (uncompressed) gbif-backbone-by-id.tsv.gz:     hash://sha256/c434c7d3622421b17dadcd119391b32a66edee59f484d4cab924d92fd17713e2     hash://sha256/e2cf9116a21966315b0482d391052223e21c8e916ae0c097dfd37bed017b815b (uncompressed)

opencc-by-4.0Oct 2022View details →

Delphi survey on clinical signs and symptoms at primary health facilities

Data from a Delphi survey conducted among 30 primary health care workers in Tanzania. The Delphi survey was based on a recent Delphi study among international experts on predictors of sepsis in children under five and included questions about each clinical element based on three domains: 1. Reliability of measurement, 2. Frequency of finding an abnormal value, and 3. Level of training required. Additionally, availability of instruments to measure vital signs and other challenges in collecting each element were evaluated. The answers were classified using a 5-point Likert scale: minimal, moderate, high, not applicable, I don’t know. The answer options for the availability of vital sign instruments were yes/no/I don’t know. We also collected data on the professional background and expertise of the participants.